1 - 10 of 25 Results
ORNL researchers have developed a deep learning-based approach to rapidly perform high-quality reconstructions from sparse X-ray computed tomography measurements.
The technologies provide additively manufactured thermal protection system.
We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.
The lack of real-time insights into how materials evolve during laser powder bed fusion has limited the adoption by inhibiting part qualification. The developed approach provides key data needed to fabricate born qualified parts.
This invention focuses on improving the ceramic yield of preceramic polymers by tuning the crosslinking process that occurs during vat photopolymerization (VP).
A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.
Using all polymer formulations, the PIP densification is improved almost 70% over traditional preceramic polymers and PIP material leading to cost and times saving for densifying ceramic composites made from powder or fibers.
The technologies provide a system and method of needling of veiled AS4 fabric tape.
This work seeks to alter the interface condition through thermal history modification, deposition energy density, and interface surface preparation to prevent interface cracking.
Additive manufacturing (AM) enables the incremental buildup of monolithic components with a variety of materials, and material deposition locations.